They are mostly based on statistical models like TF-IDF, clustering and graph-based ranking.
Generally, they directly treat keyphrase extraction as a ranking task.
For example, inspired by PageRank [1]}, [2]} propose TextRank, which is the first attempt to utilize an unsupervised graph-based approach to rank keyphrases on a word graph.
Following this framework, a substantial amount of enhanced unsupervised keyphrase extraction models are proposed via exploring various word graph from different perspectives.
For example,
SemanticRank [3]}
take account of semantic relatedness on word graphs for extracting keyphrases.
CiteTextRank [4]} and ExpandRank [5]} use neighborhood knowledge, such as citation networks [4]} and topic-related documents [5]}, to improve keyphrase extraction.
Moreover,
MIKE [8]} integrates multidimensional heterogeneous information into a unified framework to improve keyphrase extraction.
Recently,
YAKE! [9]} focuses on multi-lingual keyword extraction from single documents.
Key2vec [10]} leverages phrase embedding for keyword extraction in scientific articles.
